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20212025
most citedNavigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework

11 citations · 25 across the 8 of their papers we have counts for

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7 papers · 1 filter

cs.LG2025

Orthogonal Calibration for Asynchronous Federated Learning

Jiayun Zhang, Shuheng Li, Haiyu Huang +3

Asynchronous federated learning mitigates the inefficiency of conventional synchronous aggregation by integrating updates as they arrive and adjusting their influence based on stal…

cs.LG2024★ 9 cited

Large Language Models for Time Series: A Survey

Xiyuan Zhang, Ranak Roy Chowdhury, Rajesh K. Gupta +1

Large Language Models (LLMs) have seen significant use in domains such as natural language processing and computer vision. Going beyond text, image and graphics, LLMs present a sig…

cs.LG2023

Physics-Informed Data Denoising for Real-Life Sensing Systems

Xiyuan Zhang, Xiaohan Fu, Diyan Teng +9

Sensors measuring real-life physical processes are ubiquitous in today's interconnected world. These sensors inherently bear noise that often adversely affects performance and reli…

cs.LG2023★ 1 cited

Targeted collapse regularized autoencoder for anomaly detection: black hole at the center

Amin Ghafourian, Huanyi Shui, Devesh Upadhyay +3

Autoencoders have been extensively used in the development of recent anomaly detection techniques. The premise of their application is based on the notion that after training the a…

cs.LG2023★ 1 cited

Towards Diverse and Coherent Augmentation for Time-Series Forecasting

Xiyuan Zhang, Ranak Roy Chowdhury, Jingbo Shang +2

Time-series data augmentation mitigates the issue of insufficient training data for deep learning models. Yet, existing augmentation methods are mainly designed for classification,…

cs.LG2023★ 3 cited

Unleashing the Power of Shared Label Structures for Human Activity Recognition

Xiyuan Zhang, Ranak Roy Chowdhury, Jiayun Zhang +3

Current human activity recognition (HAR) techniques regard activity labels as integer class IDs without explicitly modeling the semantics of class labels. We observe that different…